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        <dc:title>Modelling to evaluate agricultural adaptation to climate&#13;
change in southern Australia</dc:title>
        <dc:creator>Farquharson, R</dc:creator>
        <dc:creator>Abadi, A</dc:creator>
        <dc:creator>Finlayson, J</dc:creator>
        <dc:creator>Ramilan, T</dc:creator>
        <dc:creator>Liu, D L</dc:creator>
        <dc:creator>Anwar, M</dc:creator>
        <dc:creator>Clark, S</dc:creator>
        <dc:subject>Climate Change</dc:subject>
        <dc:description>An important issue for Australian agriculture is the capacity to adapt to predicted climate change.&#13;
The International Panel on Climate Change (IPCC) (2009) refers to adaptation as ‘adjustment in natural and&#13;
human systems in response to actual or expected climate stimuli or their effects, which moderates harm or&#13;
exploits beneficial opportunities’. Conceptualizing and evaluating adaptation options in agricultural&#13;
industries should be conducted at the farming systems level (Rickards et al. (2012), Hayman et al. (2012))&#13;
because it is at this level that management decisions are made and financial as well as natural resource&#13;
impacts will be felt. In particular whole-farm analysis can represent purposeful, goal-seeking systems (Dillon&#13;
1976) to assess farmers’ profitability and the system’s sensitivity to risks such as climate variability and&#13;
change. The potential of Australian dryland agricultural systems to adapt to climate change with perennial&#13;
plants was assessed by Farquharson et al. (2013). Perennial plants have deeper rooting systems with&#13;
improved access to soil moisture, making them better suited to warmer and drier climates. Climate data were&#13;
generated using Global Circulation Models (GCMs) downscaled to specific locations and corrected for bias&#13;
(Liu and Zuo 2012). The climate data were used to estimate growth and yield of grain crops, pastures, and an&#13;
energy-tree crop using process models such as APSIM (McCown et al. (1996)) and GrassGro (Moore et al.&#13;
(1997). Plant yield and production estimates and economic data (prices of inputs and commodities) were used&#13;
in bio-economic models (MIDAS (Kingwell and Pannell 1987) and IMAGINE (Abadi and Cooper 2004)) to&#13;
identify the most profitable land use and the cash flow of options available to growers.</dc:description>
        <dc:date>2013</dc:date>
        <dc:type>Conference or Workshop Item</dc:type>
        <dc:type>PeerReviewed</dc:type>
        <dc:format>application/pdf</dc:format>
        <dc:language>en</dc:language>
        <dc:identifier>http://oar.icrisat.org/7275/1/Modelling-to-evaluate.pdf</dc:identifier>
        <dc:identifier>  Farquharson, R and Abadi, A and Finlayson, J and Ramilan, T and Liu, D L and Anwar, M and Clark, S  (2013) Modelling to evaluate agricultural adaptation to climate change in southern Australia.  In: 20th International Congress on Modelling and Simulation (MODSIM2013), 1–6 December 2013, Adelaide, Australia.     </dc:identifier>
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